
#!pip install -U scikit-learn
#!pip install -U imbalanced-learn


import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer


# Load the data direct from GitHub
df = pd.read_csv('heart_2022_with_nans.csv')
df.head()


df = df.drop('State',axis =1)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 445132 entries, 0 to 445131
Data columns (total 39 columns):
 #   Column                     Non-Null Count   Dtype  
---  ------                     --------------   -----  
 0   Sex                        445132 non-null  object 
 1   GeneralHealth              443934 non-null  object 
 2   PhysicalHealthDays         434205 non-null  float64
 3   MentalHealthDays           436065 non-null  float64
 4   LastCheckupTime            436824 non-null  object 
 5   PhysicalActivities         444039 non-null  object 
 6   SleepHours                 439679 non-null  float64
 7   RemovedTeeth               433772 non-null  object 
 8   HadHeartAttack             442067 non-null  object 
 9   HadAngina                  440727 non-null  object 
 10  HadStroke                  443575 non-null  object 
 11  HadAsthma                  443359 non-null  object 
 12  HadSkinCancer              441989 non-null  object 
 13  HadCOPD                    442913 non-null  object 
 14  HadDepressiveDisorder      442320 non-null  object 
 15  HadKidneyDisease           443206 non-null  object 
 16  HadArthritis               442499 non-null  object 
 17  HadDiabetes                444045 non-null  object 
 18  DeafOrHardOfHearing        424485 non-null  object 
 19  BlindOrVisionDifficulty    423568 non-null  object 
 20  DifficultyConcentrating    420892 non-null  object 
 21  DifficultyWalking          421120 non-null  object 
 22  DifficultyDressingBathing  421217 non-null  object 
 23  DifficultyErrands          419476 non-null  object 
 24  SmokerStatus               409670 non-null  object 
 25  ECigaretteUsage            409472 non-null  object 
 26  ChestScan                  389086 non-null  object 
 27  RaceEthnicityCategory      431075 non-null  object 
 28  AgeCategory                436053 non-null  object 
 29  HeightInMeters             416480 non-null  float64
 30  WeightInKilograms          403054 non-null  float64
 31  BMI                        396326 non-null  float64
 32  AlcoholDrinkers            398558 non-null  object 
 33  HIVTesting                 379005 non-null  object 
 34  FluVaxLast12               398011 non-null  object 
 35  PneumoVaxEver              368092 non-null  object 
 36  TetanusLast10Tdap          362616 non-null  object 
 37  HighRiskLastYear           394509 non-null  object 
 38  CovidPos                   394368 non-null  object 
dtypes: float64(6), object(33)
memory usage: 132.4+ MB


object_columns = df.select_dtypes(include=['object']).columns.tolist()
float_columns = df.select_dtypes(include=['float']).columns.tolist()
print(float_columns)
print(object_columns)

['PhysicalHealthDays', 'MentalHealthDays', 'SleepHours', 'HeightInMeters', 'WeightInKilograms', 'BMI']
['Sex', 'GeneralHealth', 'LastCheckupTime', 'PhysicalActivities', 'RemovedTeeth', 'HadHeartAttack', 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD', 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis', 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty', 'DifficultyConcentrating', 'DifficultyWalking', 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus', 'ECigaretteUsage', 'ChestScan', 'RaceEthnicityCategory', 'AgeCategory', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']


df.isnull().sum()

Sex                              0
GeneralHealth                 1198
PhysicalHealthDays           10927
MentalHealthDays              9067
LastCheckupTime               8308
PhysicalActivities            1093
SleepHours                    5453
RemovedTeeth                 11360
HadHeartAttack                3065
HadAngina                     4405
HadStroke                     1557
HadAsthma                     1773
HadSkinCancer                 3143
HadCOPD                       2219
HadDepressiveDisorder         2812
HadKidneyDisease              1926
HadArthritis                  2633
HadDiabetes                   1087
DeafOrHardOfHearing          20647
BlindOrVisionDifficulty      21564
DifficultyConcentrating      24240
DifficultyWalking            24012
DifficultyDressingBathing    23915
DifficultyErrands            25656
SmokerStatus                 35462
ECigaretteUsage              35660
ChestScan                    56046
RaceEthnicityCategory        14057
AgeCategory                   9079
HeightInMeters               28652
WeightInKilograms            42078
BMI                          48806
AlcoholDrinkers              46574
HIVTesting                   66127
FluVaxLast12                 47121
PneumoVaxEver                77040
TetanusLast10Tdap            82516
HighRiskLastYear             50623
CovidPos                     50764
dtype: int64


from sklearn.impute import SimpleImputer

# Create a SimpleImputer instance with the 'mean' strategy
imputer = SimpleImputer(strategy='mean')

# Fit the imputer to your data (Assuming X is your data)
imputer.fit(df[float_columns])

# Transform the data by filling missing values with the mean
df[float_columns] = imputer.transform(df[float_columns])


# Create a SimpleImputer instance with the 'mean' strategy
imputer1 = SimpleImputer(strategy='most_frequent')

# Fit the imputer to your data (Assuming X is your data)
imputer1.fit(df[object_columns])

# Transform the data by filling missing values with the mean
df[object_columns] = imputer1.transform(df[object_columns])


df.isnull().sum()

Sex                          0
GeneralHealth                0
PhysicalHealthDays           0
MentalHealthDays             0
LastCheckupTime              0
PhysicalActivities           0
SleepHours                   0
RemovedTeeth                 0
HadHeartAttack               0
HadAngina                    0
HadStroke                    0
HadAsthma                    0
HadSkinCancer                0
HadCOPD                      0
HadDepressiveDisorder        0
HadKidneyDisease             0
HadArthritis                 0
HadDiabetes                  0
DeafOrHardOfHearing          0
BlindOrVisionDifficulty      0
DifficultyConcentrating      0
DifficultyWalking            0
DifficultyDressingBathing    0
DifficultyErrands            0
SmokerStatus                 0
ECigaretteUsage              0
ChestScan                    0
RaceEthnicityCategory        0
AgeCategory                  0
HeightInMeters               0
WeightInKilograms            0
BMI                          0
AlcoholDrinkers              0
HIVTesting                   0
FluVaxLast12                 0
PneumoVaxEver                0
TetanusLast10Tdap            0
HighRiskLastYear             0
CovidPos                     0
dtype: int64


import matplotlib.pyplot as plt
import seaborn as sns
conditions = ['HadHeartAttack', 'HadKidneyDisease', 'HadSkinCancer']
colors = ['red', 'blue', 'orange']  # Use color names

labels = ['HeartDisease', 'KidneyDisease', 'SkinCancer']

fig, ax = plt.subplots(figsize=(17, 6))

for i, condition in enumerate(conditions):
    sns.countplot(data=df, x='AgeCategory', hue=condition, palette=colors, ax=ax)

ax.set_xlabel("AgeCategory",labelpad=40)
ax.set_ylabel("Count")
ax.legend(labels, title="Conditions", bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0.)
plt.show()


import plotly.graph_objects as go
import pandas as pd

categorical_features = ['GeneralHealth', 'LastCheckupTime', 'PhysicalActivities', 'RemovedTeeth', 'HadHeartAttack',
                        'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD', 'HadDepressiveDisorder',
                        'HadKidneyDisease', 'HadArthritis', 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',
                        'DifficultyConcentrating', 'DifficultyWalking', 'DifficultyDressingBathing', 'DifficultyErrands',
                        'SmokerStatus', 'ECigaretteUsage', 'ChestScan', 'RaceEthnicityCategory', 'AgeCategory', 'AlcoholDrinkers',
                        'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']

# Define colors for the pie charts
colours = ['#4285f4', '#ea4335', '#fbbc05', '#34a853']

def create_pie_chart(df, variable, colours):
    labels = df[variable].value_counts().index
    values = df[variable].value_counts()

    fig = go.Figure(data=[go.Pie(
        labels=labels,
        values=values,
        hole=0.35,
        textinfo='label+percent',
        marker_colors=colours
    )])

    fig.update_layout(title=variable, showlegend=False)
    fig.show()

for variable in categorical_features:
    create_pie_chart(df, variable, colours)


female_with_heart_disease = len(df[(df['HadHeartAttack']=='Yes') & (df['Sex']=='Female')])
num_female = len(df[df['Sex']=='Female'])
male_with_heart_disease = len(df[(df['HadHeartAttack']=='Yes') & (df['Sex']=='Male')])
num_male = len(df[df['Sex']=='Male'])
print('Probability of Male to have Heart disease:', male_with_heart_disease/num_male)
print('Probability of Female to have Heart disease:', female_with_heart_disease/num_female)

# Most heart disease patients are Male than Females

Probability of Male to have Heart disease: 0.07368129268444219
Probability of Female to have Heart disease: 0.041082185567185125


df = pd.get_dummies(
    df, 
    prefix_sep='_', 
    dummy_na=False, 
    drop_first=True, 
    columns=object_columns, 
    dtype='int32'
)

df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 445132 entries, 0 to 445131
Data columns (total 69 columns):
 #   Column                                                                   Non-Null Count   Dtype  
---  ------                                                                   --------------   -----  
 0   PhysicalHealthDays                                                       445132 non-null  float64
 1   MentalHealthDays                                                         445132 non-null  float64
 2   SleepHours                                                               445132 non-null  float64
 3   HeightInMeters                                                           445132 non-null  float64
 4   WeightInKilograms                                                        445132 non-null  float64
 5   BMI                                                                      445132 non-null  float64
 6   Sex_Male                                                                 445132 non-null  int32  
 7   GeneralHealth_Fair                                                       445132 non-null  int32  
 8   GeneralHealth_Good                                                       445132 non-null  int32  
 9   GeneralHealth_Poor                                                       445132 non-null  int32  
 10  GeneralHealth_Very good                                                  445132 non-null  int32  
 11  LastCheckupTime_Within past 2 years (1 year but less than 2 years ago)   445132 non-null  int32  
 12  LastCheckupTime_Within past 5 years (2 years but less than 5 years ago)  445132 non-null  int32  
 13  LastCheckupTime_Within past year (anytime less than 12 months ago)       445132 non-null  int32  
 14  PhysicalActivities_Yes                                                   445132 non-null  int32  
 15  RemovedTeeth_6 or more, but not all                                      445132 non-null  int32  
 16  RemovedTeeth_All                                                         445132 non-null  int32  
 17  RemovedTeeth_None of them                                                445132 non-null  int32  
 18  HadHeartAttack_Yes                                                       445132 non-null  int32  
 19  HadAngina_Yes                                                            445132 non-null  int32  
 20  HadStroke_Yes                                                            445132 non-null  int32  
 21  HadAsthma_Yes                                                            445132 non-null  int32  
 22  HadSkinCancer_Yes                                                        445132 non-null  int32  
 23  HadCOPD_Yes                                                              445132 non-null  int32  
 24  HadDepressiveDisorder_Yes                                                445132 non-null  int32  
 25  HadKidneyDisease_Yes                                                     445132 non-null  int32  
 26  HadArthritis_Yes                                                         445132 non-null  int32  
 27  HadDiabetes_No, pre-diabetes or borderline diabetes                      445132 non-null  int32  
 28  HadDiabetes_Yes                                                          445132 non-null  int32  
 29  HadDiabetes_Yes, but only during pregnancy (female)                      445132 non-null  int32  
 30  DeafOrHardOfHearing_Yes                                                  445132 non-null  int32  
 31  BlindOrVisionDifficulty_Yes                                              445132 non-null  int32  
 32  DifficultyConcentrating_Yes                                              445132 non-null  int32  
 33  DifficultyWalking_Yes                                                    445132 non-null  int32  
 34  DifficultyDressingBathing_Yes                                            445132 non-null  int32  
 35  DifficultyErrands_Yes                                                    445132 non-null  int32  
 36  SmokerStatus_Current smoker - now smokes some days                       445132 non-null  int32  
 37  SmokerStatus_Former smoker                                               445132 non-null  int32  
 38  SmokerStatus_Never smoked                                                445132 non-null  int32  
 39  ECigaretteUsage_Not at all (right now)                                   445132 non-null  int32  
 40  ECigaretteUsage_Use them every day                                       445132 non-null  int32  
 41  ECigaretteUsage_Use them some days                                       445132 non-null  int32  
 42  ChestScan_Yes                                                            445132 non-null  int32  
 43  RaceEthnicityCategory_Hispanic                                           445132 non-null  int32  
 44  RaceEthnicityCategory_Multiracial, Non-Hispanic                          445132 non-null  int32  
 45  RaceEthnicityCategory_Other race only, Non-Hispanic                      445132 non-null  int32  
 46  RaceEthnicityCategory_White only, Non-Hispanic                           445132 non-null  int32  
 47  AgeCategory_Age 25 to 29                                                 445132 non-null  int32  
 48  AgeCategory_Age 30 to 34                                                 445132 non-null  int32  
 49  AgeCategory_Age 35 to 39                                                 445132 non-null  int32  
 50  AgeCategory_Age 40 to 44                                                 445132 non-null  int32  
 51  AgeCategory_Age 45 to 49                                                 445132 non-null  int32  
 52  AgeCategory_Age 50 to 54                                                 445132 non-null  int32  
 53  AgeCategory_Age 55 to 59                                                 445132 non-null  int32  
 54  AgeCategory_Age 60 to 64                                                 445132 non-null  int32  
 55  AgeCategory_Age 65 to 69                                                 445132 non-null  int32  
 56  AgeCategory_Age 70 to 74                                                 445132 non-null  int32  
 57  AgeCategory_Age 75 to 79                                                 445132 non-null  int32  
 58  AgeCategory_Age 80 or older                                              445132 non-null  int32  
 59  AlcoholDrinkers_Yes                                                      445132 non-null  int32  
 60  HIVTesting_Yes                                                           445132 non-null  int32  
 61  FluVaxLast12_Yes                                                         445132 non-null  int32  
 62  PneumoVaxEver_Yes                                                        445132 non-null  int32  
 63  TetanusLast10Tdap_Yes, received Tdap                                     445132 non-null  int32  
 64  TetanusLast10Tdap_Yes, received tetanus shot but not sure what type      445132 non-null  int32  
 65  TetanusLast10Tdap_Yes, received tetanus shot, but not Tdap               445132 non-null  int32  
 66  HighRiskLastYear_Yes                                                     445132 non-null  int32  
 67  CovidPos_Tested positive using home test without a health professional   445132 non-null  int32  
 68  CovidPos_Yes                                                             445132 non-null  int32  
dtypes: float64(6), int32(63)
memory usage: 127.4 MB


import matplotlib.pyplot as plt
import pandas as pd

# Load your dataset into a DataFrame (assuming you've already done this)
# df = pd.read_csv("your_dataset.csv")

# Create a histogram for the "PhysicalHealthDays" feature
plt.hist(df["PhysicalHealthDays"], bins=20, color='blue', alpha=0.7)
plt.xlabel("Physical Health Days")
plt.ylabel("Frequency")
plt.title("Distribution of Physical Health Days")
plt.show()


correlation_matrix = df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, cmap='coolwarm', annot=True)
plt.title('Correlation Heatmap')
plt.show()


y = df['HadHeartAttack_Yes']
X=df.drop('HadHeartAttack_Yes',axis = 1)


sample_size = 4000
df1 = X[y==1].sample(n=sample_size, random_state=42)
df2 = X[y==0].sample(n=sample_size, random_state=42)
X = pd.concat([df1, df2], axis=0)   
y1 = y[y==1].sample(n=sample_size, random_state=42) 
y2 = y[y==0].sample(n=sample_size, random_state=42) 
y = pd.concat([y1, y2], axis=0)


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)


from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(random_state=42)
X_train,y_train = rus.fit_resample(X_train,y_train)


# Rescale the data using skleans standard scaler
scaler = StandardScaler()
X_scaler = scaler.fit(X_train)
X_train = X_scaler.transform(X_train)
X_test = X_scaler.transform(X_test)


print (X_train,y_train)

[[-0.13477936 -0.5393924  -0.03156063 ... -0.17937284  6.25707147
  -0.55925834]
 [ 0.34543407 -0.5393924   0.54031258 ... -0.17937284 -0.15981917
   1.7880824 ]
 [ 1.30586093 -0.5393924   0.54031258 ... -0.17937284 -0.15981917
  -0.55925834]
 ...
 [ 0.53751944  0.23124129  1.684059   ... -0.17937284 -0.15981917
   1.7880824 ]
 [ 2.26628779 -0.5393924  -1.17530706 ... -0.17937284 -0.15981917
  -0.55925834]
 [-0.19740701 -0.5393924  -0.60343385 ... -0.17937284 -0.15981917
  -0.55925834]] 145018    0
69848     0
84887     0
386731    0
443198    0
         ..
297868    1
152262    1
422587    1
347911    1
2252      1
Name: HadHeartAttack_Yes, Length: 6384, dtype: int32


from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.neural_network import MLPClassifier
from scipy.stats import expon
import numpy as np
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import make_scorer, recall_score
import tensorflow as tf
from tensorflow import keras
# Define hyperparameter grids for Randomized Search
y_prob1 ={}
param_grids_random = {
    "Logistic Regression": {
        'C': expon(scale=1),
        'penalty': ['l1', 'l2'],
        'solver': ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga'],
    },
    "Decision Tree": {
        'max_depth': np.arange(1, 21),
        'min_samples_split': np.arange(2, 11),
        'min_samples_leaf': np.arange(1, 11),
        'max_features': ['auto', 'sqrt', 'log2']
    },
    "Random Forest": {
        'n_estimators': np.arange(100, 1000, 100),
        'max_features': ['auto', 'sqrt', 'log2'],
        'max_depth': np.arange(10, 110, 10),
        'min_samples_split': np.arange(2, 11),
        'min_samples_leaf': np.arange(1, 11)
    },
    "SVM": {
        'C': expon(scale=1),
        'kernel': ['linear', 'poly', 'rbf', 'sigmoid']
    },
    "XGBoost": {
        'n_estimators': np.arange(100, 1000, 100),
        'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3],
        'max_depth': np.arange(3, 11),
        'subsample': [0.8, 0.9, 1.0],
    },
    "AdaBoost": {
        'n_estimators': np.arange(50, 251, 50),
        'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3]
    },
    }


# Define a dictionary to store the classifiers
classifiers = {
    "Logistic Regression": LogisticRegression(),
    "Decision Tree": DecisionTreeClassifier(),
    "Random Forest": RandomForestClassifier(),
    "SVM": SVC(),
    "XGBoost": XGBClassifier(),
    "AdaBoost": AdaBoostClassifier(),
}

# Initialize evaluation metrics lists
accuracy_scores_grid = {}
precision_scores_grid = {}
recall_scores_grid = {}
f1_scores_grid = {}
roc_auc_scores_grid = {}

# Perform Randomized Search and Grid Search for each classifier
for name, classifier in classifiers.items():
    # Define hyperparameter grid for Randomized Search
    param_grid_random = param_grids_random.get(name, {})

    if param_grid_random:
        # Create a Randomized Search object
        random_search = RandomizedSearchCV(classifier, param_distributions=param_grid_random, n_iter=5, scoring=make_scorer(recall_score), cv=5, n_jobs=-1, random_state=42)

        # Fit the Randomized Search to the training data
        random_search.fit(X_train, y_train)

        # Get the best parameters from Randomized Search
        best_params_random1 = random_search.best_params_
        
        best_params_random = {}
        
        for i in best_params_random1.keys():
            best_params_random[i] = [best_params_random1[i]]

        # Create a Grid Search object using the best parameters from Randomized Search
        grid_search = GridSearchCV(classifier, best_params_random, scoring=make_scorer(recall_score), cv=3, n_jobs=-1)

        # Fit the Grid Search to the training data
        grid_search.fit(X_train, y_train)

        # Get the best model from Grid Search
        best_model_grid = grid_search.best_estimator_

        # Make predictions on the test data using the best model from Grid Search
        y_pred_grid = best_model_grid.predict(X_test)
        y_prob1[name] = y_pred_grid
        
        # Calculate evaluation metrics for the model from Grid Search
        accuracy_grid = accuracy_score(y_test, y_pred_grid)
        precision_grid = precision_score(y_test, y_pred_grid)
        recall_grid = recall_score(y_test, y_pred_grid)
        f1_grid = f1_score(y_test, y_pred_grid)
        roc_auc_grid = roc_auc_score(y_test, y_pred_grid)

        # Store metrics in the dictionaries
        accuracy_scores_grid[name] = accuracy_grid
        precision_scores_grid[name] = precision_grid
        recall_scores_grid[name] = recall_grid
        f1_scores_grid[name] = f1_grid
        roc_auc_scores_grid[name] = roc_auc_grid

C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning:


15 fits failed out of a total of 25.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
10 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.


C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning:

One or more of the test scores are non-finite: [       nan        nan        nan 0.75938992 0.76095535]

C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning:


10 fits failed out of a total of 25.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
7 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 1145, in wrapper
    estimator._validate_params()
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 638, in _validate_params
    validate_parameter_constraints(
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\utils\_param_validation.py", line 96, in validate_parameter_constraints
    raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.

--------------------------------------------------------------------------------
3 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 1145, in wrapper
    estimator._validate_params()
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 638, in _validate_params
    validate_parameter_constraints(
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\utils\_param_validation.py", line 96, in validate_parameter_constraints
    raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.


C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning:

One or more of the test scores are non-finite: [       nan 0.7136631  0.67073601        nan 0.71209472]

C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning:


5 fits failed out of a total of 25.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 1145, in wrapper
    estimator._validate_params()
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\base.py", line 638, in _validate_params
    validate_parameter_constraints(
  File "C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\utils\_param_validation.py", line 96, in validate_parameter_constraints
    raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.


C:\Users\abhin\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning:

One or more of the test scores are non-finite: [0.79948784 0.78664596 0.78288176        nan 0.79823735]


from sklearn.metrics import roc_curve

# Print evaluation metrics for Grid Search using the best parameters from Randomized Search
print("Grid Search Results using the best parameters from Randomized Search:")
for name, accuracy in accuracy_scores_grid.items():
    print(f"{name} - Accuracy: {accuracy:.2f}")

for name, precision in precision_scores_grid.items():
    print(f"{name} - Precision: {precision:.2f}")

for name, recall in recall_scores_grid.items():
    print(f"{name} - Recall: {recall:.2f}")

for name, f1 in f1_scores_grid.items():
    print(f"{name} - F1-Score: {f1:.2f}")
    


for name, roc_auc in roc_auc_scores_grid.items():
    y_prob = best_model_grid.predict_proba(X_test)[:, 1]
    fpr, tpr, thresholds = roc_curve(y_test, y_prob)
    roc_auc = roc_auc_score(y_test, y_prob1[name])
    print(f"{name} - ROC-AUC: {roc_auc:.2f}")
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f"ROC curve (AUC = {roc_auc:.2f})")
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel("False Positive Rate")
    plt.ylabel("True Positive Rate")
    plt.title(f"Receiver Operating Characteristic (ROC) for {name}")
    plt.legend(loc="lower right")
    plt.show()

Grid Search Results using the best parameters from Randomized Search:
Logistic Regression - Accuracy: 0.79
Decision Tree - Accuracy: 0.73
Random Forest - Accuracy: 0.80
SVM - Accuracy: 0.80
XGBoost - Accuracy: 0.79
AdaBoost - Accuracy: 0.79
Logistic Regression - Precision: 0.80
Decision Tree - Precision: 0.73
Random Forest - Precision: 0.79
SVM - Precision: 0.82
XGBoost - Precision: 0.80
AdaBoost - Precision: 0.82
Logistic Regression - Recall: 0.77
Decision Tree - Recall: 0.74
Random Forest - Recall: 0.83
SVM - Recall: 0.78
XGBoost - Recall: 0.79
AdaBoost - Recall: 0.76
Logistic Regression - F1-Score: 0.79
Decision Tree - F1-Score: 0.73
Random Forest - F1-Score: 0.81
SVM - F1-Score: 0.80
XGBoost - F1-Score: 0.80
AdaBoost - F1-Score: 0.79
Logistic Regression - ROC-AUC: 0.79

Decision Tree - ROC-AUC: 0.73

Random Forest - ROC-AUC: 0.80

SVM - ROC-AUC: 0.80

XGBoost - ROC-AUC: 0.79

AdaBoost - ROC-AUC: 0.79


from sklearn.metrics import confusion_matrix

for name, roc_auc in roc_auc_scores_grid.items():
    cnf_matrix = confusion_matrix(y_test, y_prob1[name])
    p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap="YlGnBu" ,fmt='g')
    plt.title(name + " "+ 'Confusion matrix', y=1.1)
    plt.ylabel('Actual label')
    plt.xlabel('Predicted label')
    plt.show()

Model Evaluation Results¶

Accuracy¶

Precision¶

Recall¶

F1-Score¶

ROC-AUC (Receiver Operating Characteristic - Area Under the Curve)¶

Conclusion¶

Model Performance Metrics¶

Importance of Evaluation Metrics¶

1. Accuracy - Measuring Overall Correctness¶

2. Precision - Emphasizing True Positives¶

3. Recall - Sensitivity to True Positives¶

4. F1-Score - Balancing Precision and Recall¶

5. ROC-AUC - Discriminating Ability¶

Maximizing Each Metric in the Context of healthcare¶

The Significance of Recall¶

The Best Model Choice¶

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	HeightInMeters	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos
0	Alabama	Female	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	NaN	No	...	NaN	NaN	NaN	No	No	Yes	No	Yes, received tetanus shot but not sure what type	No	No
1	Alabama	Female	Excellent	0.0	0.0	NaN	No	6.0	NaN	No	...	1.60	68.04	26.57	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	No
2	Alabama	Female	Very good	2.0	3.0	Within past year (anytime less than 12 months ...	Yes	5.0	NaN	No	...	1.57	63.50	25.61	No	No	No	No	NaN	No	Yes
3	Alabama	Female	Excellent	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	NaN	No	...	1.65	63.50	23.30	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No
4	Alabama	Female	Fair	2.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	NaN	No	...	1.57	53.98	21.77	Yes	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	No

	PhysicalHealthDays	MentalHealthDays	SleepHours	HeightInMeters	WeightInKilograms	BMI	GeneralHealth_Fair	...	AlcoholDrinkers_Yes	FluVaxLast12_Yes	PneumoVaxEver_Yes	TetanusLast10Tdap_Yes, received tetanus shot but not sure what type	CovidPos_Yes
0	0.0	0.0	8.0	1.702691	83.07447	28.529842	0	...	0	1	0	1	0
1	0.0	0.0	6.0	1.600000	68.04000	26.570000	0	...	0	0	0	0	0
2	2.0	3.0	5.0	1.570000	63.50000	25.610000	0	...	0	0	0	0	1
3	0.0	0.0	7.0	1.650000	63.50000	23.300000	0	...	0	1	1	0	0
4	2.0	0.0	9.0	1.570000	53.98000	21.770000	1	...	1	0	1	0	0